Dealing With And Avoiding Chronic Disease Management Burnout

I’ve been thinking about juggling lately, especially as this year I’ve had to add a series of new habits and behaviors and medications to manage not one but two new chronic diseases. Getting one new chronic disease is hard; getting another is hard; and the challenges aren’t necessarily linear or exponential, and they’re not necessarily obvious up front.

But sometimes the challenges do compound over time.

In January when I started taking pancreatic enzyme replacement therapy (PERT) for exocrine pancreatic insufficiency (EPI or PEI), I had to teach myself to remember to take enzymes at every meal. Not just some time around the meal, but 100% every time before (by only a few minutes) or right at the start of the meal. With PERT, the timing matters for efficacy. I have a fast/short feedback loop – if I mis-time my enzymes or don’t take them, I get varying symptoms within a few hours that then bother me for the rest of the day, overnight, and into the next morning. So I’m very incentivized to take the enzymes and time them effectively when I eat. However, as I started to travel (my first trip out of the country since the pandemic started), I was nervous about trying to adapt to travel and being out of my routine at home where I’ve placed enzymes in visible eye sight of every location where I might consume food. Thankfully, that all went well and I managed not to forget taking enzymes when I ate and all was well. But I know I’m still building the habit of taking enzymes and eating, and that involves both always having enzymes with me and remembering to get them out and take them. It sounds like a trivial amount of things to remember, but this is added on top of everything else I’m doing for managing my health and well-being.

This includes other “simple” things like taking my allergy medications – because I’m allergic to cats (and we have them!), trees, dust, etc. And vitamins (I’m vitamin D deficient when I don’t take vitamin D).

And brushing my teeth and flossing.

You do that too, right? Or maybe you’re one of those people who struggle to remember to floss. It’s normal.

The list of well-being management gets kind of long when you think about all the every day activities and habits you have to help you stay at your best possible health.

Eat healthy! (You do that, right? 😉 )

Hydrate!

Exercise!

Etc.

I’ve also got the background habits of 20 years of living with diabetes: keeping my pump sites on my body; refilling the reservoir and changing the pump site every few days; making sure the insulin doesn’t get too hot or cold; making sure my CGM data isn’t too noisy; changing my CGM sensor when needed; estimating ballpark carbs and entering them and/or temporary targets to indicate exercise into my open source AID; keeping my AID powered; keeping my pump powered; keeping my phone – which has my CGM visibility on it – powered and nearby. Ordering supplies – batteries and pump sites and reservoirs and CGM transmitters and CGM sensors and insulin and glucagon.

Some of these are daily or every few days tasks; others are once or twice a month or every three months.

Those stack up sometimes where I need to refill a reservoir and oops, get another bottle of insulin out of the fridge which reminds me to make a note to check on my shipment of insulin which hasn’t arrived yet. I also need to change my pump site and my CGM sensor is expiring at bedtime so I need to also go ahead and change it so the CGM warmup period will be done by the time I go to sleep. I want to refill my reservoir and change the pump site after dinner since the dinner insulin is more effective on the existing site; I think of this as I pull my enzymes out to swallow as I start eating. I’ll do the CGM insertion when I do my pump site change. But the CGM warmup period is then in the after-dinner timeframe so I then have to keep an eye on things manually because my AID can’t function without CGM data so 2 hours (or more) of warmup means extra manual diabetes attention. While I’m doing that, I also need to remember to take my allergy medication and vitamin D, plus remembering to take my new thyroid medication at bedtime.

Any given day, that set of overlapping scenarios may be totally fine, and I don’t think anything of them.

On other days, where I might be stressed or overwhelmed by something else – even if it’s not health-related – that can make the above scenario feel overwhelmingly difficult.

One of the strategies I discussed in a previous post relative to planning travel or busy periods like holidays is trying to separate tasks in advance (like pre-filling a reservoir), so the action tasks (inserting a pump site and hooking it up to a new reservoir) don’t take as long. That works well, if you know the busy period is coming.

But sometimes you don’t have awareness of a forthcoming busy period and life happens. Or it’s not necessarily busy, per se, but you start to get overwhelmed and stressed and that leaks over into the necessary care and feeding of medical stuff, like managing pump sites and reservoirs and sensors and medication.

You might start negotiating with yourself: “do I really need to change that pump site today? It can wait until tomorrow”. Or you might wait until your reservoir actually hits the ‘0’ level (which isn’t fully 0; there’s a few units plus or minus some bubbles left) to refill it. Or other things like that, whether it’s not entering carbs into your pump or AID or not bolusing. Depending on your system/setup, those things may not be a big deal. And for a day or two, they’re likely not a big deal overall.

But falling into the rut of these becoming the new normal is not optimal – that’s burnout, and I try to avoid getting there.

When I start to have some of those thought patterns and recognize that I have begun negotiating with myself, I try to voice how I’m feeling to myself and my spouse or family or friends. I tell them I’m starting to feel “crispy” (around the edges) – indicating I’m not fully burnt out, but I could get all the way to burnout if I don’t temporarily change some things. (Or permanently, but often for me temporary shifts are effective.)

One of the first things I do is think through what is the bare minimum necessary care I need to take. I go above and beyond and optimize a LOT of things to get above-target outcomes in most areas. While I like to do those things, they’re not necessary. So I think through the list of necessary things, like: keeping a working pump site on my body; keeping insulin in a reservoir attached to my pump; keeping my CGM sensor working; and keeping my AID powered and nearby.

That then leaves a pile of tasks to consider:

  1. Not doing at all for ___ period of time
  2. Not doing myself but asking someone else to do for ____ period of time

And then I either ask or accept the offers of help I get to do some of those things.

When I was in high school and college, I would have weekends where I would ask my parents to help. They would take on the task of carb counting (or estimating) so I didn’t have to. (They also did HEAPS of work for years while I was on their insurance to order and keep supplies in the house and wrangle with insurance so I didn’t have to – that was huge background help that I greatly appreciated.)

Nowadays, there are still things I can and do get other people to help with. Sometimes it’s listening to me vent (with a clear warning that I’m just venting and don’t need suggestions); my parents often still fill that role for me! Since I’m now married and no longer living alone, Scott offers a lot of support especially during those times. Sometimes he fills reservoirs for me, or more often will bring me supplies from the cabinet or fridge to wherever I’m sitting (or even in bed so I don’t have to get up to go change my site). Or he’ll help evaluate and determine that something can wait until a later time to do (e.g. change pump site at another time). Sometimes I get him to open boxes for me and we re-organize how my supplies are to make them easier to grab and go.

Those are diabetes-specific examples, but I’ve also written about how helpful additional help can be sometimes for EPI too, especially with weighing and estimating macronutrient counts so I can figure out my PERT dosing. Or making food once I’ve decided what I want to eat, again so I can separate deciding what to eat and what the counts/dosing is from the action tasks of preparing or cooking the food.

For celiac, one of the biggest changes that has helped was Scott asking family members to load the “Find Me Gluten Free” app on their phone. That way, if we were going out to eat or finding a takeout option, instead of everyone ALWAYS turning to me and saying “what are the gluten free options?”, they could occasionally also skim the app to see what some of the obvious choices were, so I wasn’t always having to drive the family decision making on where to eat.

If you don’t have a chronic illness (or multiple chronic illnesses), these might not sound like a big deal. If you do (even if you have a different set of chronic disease(s)), maybe you recognize some of this.

There are estimates that people with diabetes make hundreds of decisions and actions a day for managing living with diabetes. Multiply that times 20 years. Ditto for celiac, for identifying and preparing and guarding against cross-contamination of said gluten-free food – multiply that work every day times 14 years. And now a year’s worth of *every* time I consider eating anything to estimate (with reading nutrition labels or calculating combinations based on food labels or weighing and googling and estimating compared to other nutrition labels) how much enzymes to take and remembering to swallow the right number of pills at the optimal times. Plus the moral and financial weight of deciding how to balance efficacy with cost of these enzymes. Plus several months now of an additional life-critical medication.

It’s so much work.

It’s easy to get outright burnt out, and common to start to feel a little “crispy” around the edges at times.

If you find yourself in this position, know that it’s normal.

You’re doing a lot, and you’re doing a great job to keep yourself alive.

You can’t do 110% all the time, though, so it is ok to figure out what is the bare minimum and some days throughout the year, just do that, so you can go back to 110%-ing it (or 100%-ing) the other days.

With practice, you will increasingly be able to spot patterns of scenarios or times of the year when you typically get crispy, and maybe you can eventually figure out strategies to adapt in advance (see me over here pre-filling reservoirs ahead of Thanksgiving last week and planning when I’d change my pump site and planning exactly what I would eat for 3 days).

TLDR:

  • Living with chronic disease is hard. And the more diseases you have, the harder it can be.
  • If you live with or love someone with chronic disease(s), ask them if you can help. If they’re venting, ask if they want you to listen (valuable!) or to let you know if at any point they want help brainstorming or for you to provide suggestions (helpful *if* desired and requested).
  • If you’re the one living with chronic disease(s), consider asking for help, even with small things. Don’t let your own judgment (“I should be able to do this!”) get in your way of asking for help. Try it for a day or for a weekend.
Dealing with and avoiding chronic disease burnout by Dana M. Lewis

Regulatory Approval Is A Red Herring

One of the most common questions I have been asked over the last 8 years is whether or not we are submitting OpenAPS to the FDA for regulatory approval.

This question is a big red herring.

Regulatory approval is often seen and discussed as the one path for authenticating and validating safety and efficacy.

It’s not the only way.

It’s only one way.

As background, you need to understand what OpenAPS is. We took an already-approved insulin pump that I already had, a continuous glucose monitor (CGM) that I already had, and found a way to read data from those devices and also to use the already-built commands in the pump to send back instructions to automate insulin delivery via the decision-making algorithm that we created. The OpenAPS algorithm was the core innovation, along with the realization that this already-approved pump had those capabilities built in. We used various off the shelf hardware (mini-computers and radio communication boards) to interoperate with my already approved medical devices. There was novelty in how we put all the pieces together, though the innovation was the algorithm itself.

The caveat, though, is that although the pump I was using was regulatory-approved and on the market, which is how I already had it, it had later been recalled after researchers, the manufacturer, and the FDA realized that you could use the already-built commands in the pump’s infrastructure. So these pumps, while not causing harm to anyone and no cases of harm have ever been recorded, were no longer being sold. It wasn’t a big deal to the company; it was a voluntary recall, and people like me often chose to keep our pumps if we were not concerned about this potential risk.

We had figured out how to interoperate with these other devices. We could have taken our system to the FDA. But because we were using already-off-the-market pumps, there was no way the FDA would approve it. And at the time (circa 2014), there was no vision or pathway for interoperable devices, so they didn’t have the infrastructure to approve “just” an automated insulin delivery algorithm. (That changed many years later and they now have infrastructure for reviewing interoperable pumps, CGM, and algorithms which they call controllers).

The other relevant fact is that the FDA has jurisdiction based on the commerce clause in the US Constitution: Congress used its authority to authorize the FDA to regulate interstate commerce in food, drugs, and medical devices. So if you’re intending to be a commercial entity and sell products, you must submit for regulatory approval.

But if you’re not going to sell products…

This is the other aspect that many people don’t seem to understand. All roads do not lead to regulatory approval because not everyone wants to create a company and spend 5+ years dedicating all their time to it. That’s what we would have had to do in order to have a company to try to pursue regulatory approval.

And the key point is: given such a strict regulatory environment, we (speaking for Dana and Scott) did not want to commercialize anything. Therefore there was no point in submitting for regulatory approval. Regardless of whether or not the FDA was likely to approve given the situation at the time, we did not want to create a company, spend years of our life dealing with regulatory and compliance issues full time, and maybe eventually get permission to sell a thing (that we didn’t care about selling).

The aspect of regulatory approval is a red herring in the story of the understanding of OpenAPS and the impact it is having and could have.

Yes, we could have created a company. But then we would not have been able to spend the thousands of hours that we spent improving the system we made open source and helping thousands of individuals who were able to use the algorithm and subsequent systems with a variety of pumps, CGMs, and mobile devices as an open source automated insulin delivery system. We intentionally chose this path to not commercialize and thus not to pursue regulatory approval.

As a result of our work (and others from the community), the ecosystem has now changed.

Time has also passed: it’s been 8 years since I first automated insulin delivery for myself!

The commercial players have brought multiple commercial AIDs to market now, too.

We created OpenAPS when there was NO commercial option at the time. Now there are a few commercial options.

But it is also an important note that I, and many thousands of other people, are still choosing to use open source AID systems.

Why?

This is another aspect of the red herring of regulatory approval.

Just because something is approved does not mean it’s available to order.

If it’s available to order (and not all countries have approved AID systems!), it doesn’t mean it’s accessible or affordable.

Insurance companies are still fighting against covering pumps and CGMs as standalone devices. New commercial AID systems are even more expensive, and the insurance companies are fighting against coverage for them, too. So just because someone wants an AID and has one approved in their country doesn’t mean that they will be able to access and/or afford it. Many people with diabetes struggle with the cost of insulin, or the cost of CGM and/or their insulin pump.

Sometimes providers refuse to prescribe devices, based on preconceived notions (and biases) about who might do “well” with new therapies based on past outcomes with different therapies.

For some, open source AID is still the most accessible and affordable option.

And in some places, it is still the ONLY option available to automate insulin delivery.

(And in most places, open source AID is still the most advanced, flexible, and customizable option.)

Understanding the many reasons why someone might choose to use open source automated insulin delivery folds back into the understanding of how someone chooses to use open source automated insulin delivery.

It is tied to the understanding that manual insulin delivery – where someone makes all the decisions themselves and injects or presses buttons manually to deliver insulin – is inherently risky.

Automated insulin delivery reduces risk compared to manual insulin delivery. While some new risk is introduced (as is true of any additional devices), the net risk reduction overall is significantly large compared to manual insulin delivery.

This net risk reduction is important to contextualize.

Without automated insulin delivery, people overdose or underdose on insulin multiple times a day, causing adverse effects and bad outcomes and decreasing their quality of life. Even when they’re doing everything right, this is inevitable because the timing of insulin is so challenging to manage alongside dozens of other variables that at every decision point play a role in influencing the glucose outcomes.

With open source automated insulin delivery, it is not a single point-in-time decision to use the system.

Every moment, every day, people are actively choosing to use their open source automated insulin delivery system because it is better than the alternative of managing diabetes manually without automated insulin delivery.

It is a conscious choice that people make every single day. They could otherwise choose to not use the automated components and “fall back” to manual diabetes care at any moment of the day or night if they so choose. But most don’t, because it is safer and the outcomes are better with automated insulin delivery.

Each individual’s actions to use open source AID on an ongoing basis are data points on the increased safety and efficacy.

However, this paradigm of patient-generated data and patient choice as data contributing toward safety and efficacy is new. There are not many, if any, other examples of patient-developed technology that does not go down the commercial path, so there are not a lot of comparisons for open source AID systems.

As a result, when there were questions about the safety and efficacy of the system (e.g., “how do you know it works for someone else other than you, Dana?”), we began to research as a community to address the questions. We published data at the world’s biggest scientific conference and were peer-reviewed by scientists and accepted to present a poster. We did so. We were cited in a piece in Nature as a result. We then were invited to submit a letter to the editor of a traditional diabetes journal to summarize our findings; we did so and were published.

I then waited for the rest of the research community to pick up this lead and build on the work…but they didn’t. I picked it up again and began facilitating research directly with the community, coordinating efforts to make anonymized pools of data for individuals with open source AID to submit their data to and for years have facilitated access to dozens of researchers to use this data for additional research. This has led to dozens of publications further documenting the efficacy of these solutions.

Yet still, there was concern around safety because the healthcare world didn’t know how to assess these patient-generated data points of choice to use this system because it was better than the alternative every single day.

So finally, as a direct result of presenting this community-based research again at the world’s largest diabetes scientific conference, we were able to collaborate and design a grant proposal that received grant funding from New Zealand’s Health Research Council (the equivalent of the NIH in the US) for a randomized control trial of the OpenAPS algorithm in an open source AID system.

An RCT is often seen as the gold standard in science, so the fact that we received funding for such a study alone was a big milestone.

And this year, in 2022, the RCT was completed and our findings were published in one of the world’s largest medical journals, the New England Journal of Medicine, establishing that the use of the OpenAPS algorithm in an open source AID was found to be safe and effective in children and adults.

No surprises here, though. I’ve been using this system for more than 8 years, and seeing thousands of others choose the OpenAPS algorithm on an ongoing, daily basis for similar reasons.

So today, it is possible that someone could take an open source AID system using the OpenAPS algorithm to the FDA for regulatory approval. It won’t likely be me, though.

Why not? The same reasons apply from 8 years ago: I am not a company, I don’t want to create a company to be able to sell things to end users. The path to regulatory approval primarily matters for those who want to sell commercial products to end users.

Also, regulatory approval (if someone got the OpenAPS algorithm in an open source AID or a different algorithm in an open source AID) does not mean it will be commercially available, even if it will be approved.

It requires a company that has pumps and CGMs it can sell alongside the AID system OR commercial partnerships ready to go that are able to sell all of the interoperable, approved components to interoperate with the AID system.

So regulatory approval of an AID system (algorithm/mobile controller design) without a commercial partnership plan ready to go is not very meaningful to people with diabetes in and of itself. It sounds cool, but will it actually do anything? In and of itself, no.

Thus, the red herring.

Might it be meaningful eventually? Yes, possibly, especially if we collectively have insurers to get over themselves and provide coverage for AID systems given that AID systems all massively improve short-term and long-term outcomes for people with diabetes.

But as I said earlier, regulatory approval does necessitate access nor affordability, so an approved system that’s not available and affordable to people is not a system that can be used by many.

We have a long way to go before commercial AID systems are widely accessible and affordable, let alone available in every single country for people with diabetes worldwide.

Therefore, regulatory approval is only one piece of this puzzle.

And it is not the only way to assess safety and efficacy.

The bigger picture this has shown me over the years is that while systems are created to reduce harm toward people – and this is valid and good – there have been tendencies to convert to the assumption that therefore the systems are the only way to achieve the goal of harm reduction or to assess safety and efficacy.

They aren’t the only way.

As explained above, FDA approval is one method of creating a rubber stamp as a shorthand for “is this considered to be safe and effective”.

That’s also legally necessary for companies to use if they want to sell products. For situations that aren’t selling products, it’s not the only way to assess safety and efficacy, which we have shown with OpenAPS.

With open source automated insulin delivery systems, individuals have access to every line of code and can test and choose for themselves, not just once, but every single day, whether they consider it to be safer and more effective for them than manual insulin dosing. Instead of blindly trusting a company, they get the choice to evaluate what they’re using in a different way – if they so choose.

So any questions around seeking regulatory approval are red herrings.

A different question might be: What’s the future of the OpenAPS algorithm?

The answer is written in our OpenAPS plain language reference design that we posted in February of 2015. We detailed our vision for individuals like us, researchers, and companies to be able to use it in the future.

And that’s how it’s being used today, by 1) people like me; and 2)  in research, to improve what we can learn about diabetes itself and improve AID; and 3) by companies, one of whom has already incorporated parts of our safety design as part of a safety layer in their ML-based AID system and has CE mark approval and is being sold and used by thousands of people in Europe.

It’s possible that someone will take it for regulatory approval; but that’s not necessary for the thousands of people already using it. That may or may not make it more available for thousands more (see earlier caveats about needing commercial partnerships to be able to interoperate with pumps and CGMs).

And regardless, it is still being used to change the world for thousands of people and help us learn and understand new things about the physiology of diabetes because of the way it was designed.

That’s how it’s been used and that’s the future of how it will continue to be used.

No rubber stamps required.

Regulatory Approval: A Red Herring

Understanding the Difference Between Open Source and DIY in Diabetes

There’s been a lot of excitement (yay!) about the results of the CREATE trial being published in NEJM, followed by the presentation of the continuation results at EASD. This has generated a lot of blog posts, news articles, and discussion about what was studied and what the implications are.

One area that I’ve noticed is frequently misunderstood is how “open source” and “DIY” are different.

Open source means that the source code is openly available to view. There are different licenses with open source; most allow you to also take and reuse and modify the code however you like. Some “copy-left” licenses commercial entities to open-source any software they build using such code. Most companies can and do use open source code, too, although in healthcare most algorithms and other code related to FDA-regulated activity is proprietary. Most open source licenses allow free individual use.

For example, OpenAPS is open source. You can find the core code of the algorithm here, hosted on Github, and read every line of code. You can take it, copy it, use it as-is or modify it however you like, because the MIT license we put on the code says you can!

As an individual, you can choose to use the open source code to “DIY” (do-it-yourself) an automated insulin delivery system. You’re DIY-ing, meaning you’re building it yourself rather than buying it or a service from a company.

In other words, you can DIY with open source. But open source and DIY are not the same thing!

Open source can and is usually is used commercially in most industries. In healthcare and in diabetes specifically, there are only a few examples of this. For OpenAPS, as you can read in our plain language reference design, we wanted companies to use our code as well as individuals (who would DIY with it). There’s at least one commercial company now using ideas from the OpenAPS codebase and our safety design as a safety layer against their ML algorithm, to make sure that the insulin dosing decisions are checked against our safety design. How cool!

However, they’re a company, and they have wrapped up their combination of proprietary software and the open source software they have implemented, gotten a CE mark (European equivalent of FDA approval), and commercialized and sold their AID product to people with diabetes in Europe. So, those customers/users/people with diabetes are benefitting from open source, although they are not DIY-ing their AID.

Outside of healthcare, open source is used far more pervasively. Have you ever used Zoom? Zoom uses open source; you then use Zoom, although not in a DIY way. Same with Firefox, the browser. Ever heard of Adobe? They use open source. Facebook. Google. IBM. Intel. LinkedIn. Microsoft. Netflix. Oracle. Samsung. Twitter. Nearly every product or service you use is built with, depends on, or contains open source components. Often times open source is more commonly used by companies to then provide products to users – but not always.

So, to more easily understand how to talk about open source vs DIY:

  • The CREATE trial used a version of open source software and algorithm (the OpenAPS algorithm inside a modified version of the AndroidAPS application) in the study.
  • The study was NOT on “DIY” automated insulin delivery; the AID system was handed/provided to participants in the study. There was no DIY component in the study, although the same software is used both in the study and in the real world community by those who do DIY it. Instead, the point of the trial was to study the safety and efficacy of this version of open source AID.
  • Open source is not the same as DIY.
  • OpenAPS is open source and can be used by anyone – companies that want to commercialize, or individuals who want to DIY. For more information about our vision for this, check out the OpenAPS plain language reference design.
Venn diagram showing a small overlap between a bigger open source circle and a smaller DIY circle. An arrow points to the overlapping section, along with text of "OpenAPS". Below it text reads: "OpenAPS is open source and can be used DIY. DIY in diabetes often uses open source, but not always. Not all open source is used DIY."

Continuation Results On 48 Weeks of Use Of Open Source Automated Insulin Delivery From the CREATE Trial: Safety And Efficacy Data

In addition to the primary endpoint results from the CREATE trial, which you can read more about in detail here or as published in the New England Journal of Medicine, there was also a continuation phase study of the CREATE trial. This meant that all participants from the CREATE trial, including those who were randomized to the automated insulin delivery (AID) arm and those who were randomized to sensor-augmented insulin pump therapy (SAPT, which means just a pump and CGM, no algorithm), had the option to continue for another 24 weeks using the open source AID system.

These results were presented by Dr. Mercedes J. Burnside at #EASD2022, and I’ve summarized her presentation and the results below on behalf of the CREATE study team.

What is the “continuation phase”?

The CREATE trial was a multi-site, open-labeled, randomized, parallel-group, 24-week superiority trial evaluating the efficacy and safety of an open-source AID system using the OpenAPS algorithm in a modified version of AndroidAPS. Our study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14 percentage points higher among those who used the open-source AID system (95% confidence interval [CI], 9.2 to 18.8; P<0.001) compared to those who used sensor augmented pump therapy; a difference that corresponds to 3 hours 21 minutes more time spent in target range per day. The system did not contribute to any additional hypoglycemia. Glycemic improvements were evident within the first week and were maintained over the 24-week trial. This illustrates that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID. This initial study concluded that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS, a widely used open-source AID solution, is efficacious and safe. These results were from the first 24-week phase when the two groups were randomized into SAPT and AID, accordingly.

The second 24-week phase is known as the “continuation phase” of the study.

There were 52 participants who were randomized into the SAPT group that chose to continue in the study and used AID for the 24 week continuation phase. We refer to those as the “SAPT-AID” group. There were 42 participants initially randomized into AID who continued to use AID for another 24 weeks (the AID-AID group).

One slight change to the continuation phase was that those in the SAPT-AID used a different insulin pump than the one used in the primary phase of the study (and 18/42 AID-AID participants also switched to this different pump during the continuation phase), but it was a similar Bluetooth-enabled pump that was interoperable with the AID system (app/algorithm) and CGM used in the primary outcome phase.

All 42 participants in AID-AID completed the continuation phase; 6 participants (out of 52) in the SAPT-AID group withdrew. One withdrew from infusion site issues; three with pump issues; and two who preferred SAPT.

What are the results from the continuation phase?

In the continuation phase, those in the SAPT-AID group saw a change in time in range (TIR) from 55±16% to 69±11% during the continuation phase when they used AID. In the SAPT-AID group, the percentage of participants who were able to achieve the target goals of TIR > 70% and time below range (TBR) <4% increased from 11% of participants during SAPT use to 49% during the 24 week AID use in the continuation phase. Like in the primary phase for AID-AID participants; the SAPT-AID participants saw the greatest treatment effect overnight with a TIR difference of 20.37% (95% CI, 17.68 to 23.07; p <0.001), and 9.21% during the day (95% CI, 7.44 to 10.98; p <0.001) during the continuation phase with open source AID.

Those in the AID-AID group, meaning those who continued for a second 24 week period using AID, saw similar TIR outcomes. Prior to AID use at the start of the study, TIR for that group was 61±14% and increased to 71±12% at the end of the primary outcome phase; after the next 6 months of the continuation phase, TIR was maintained at 70±12%. In this AID-AID group, the percentage of participants achieving target goals of TIR >70% and TBR <4% was 52% of participants in the first 6 months of AID use and 45% during the continuation phase. Similarly to the primary outcomes phase, in the continuation phase there was also no treatment effect by age interaction (p=0.39).

The TIR outcomes between both groups (SAPT-AID and AID-AID) were very similar after each group had used AID for 24 weeks (SAPT-AID group using AID for 24 weeks during the continuation phase and AID-AID using AID for 24 weeks during the initial RCT phase).. The adjusted difference in TIR between these groups was 1% (95% CI, -4 to 6; p=-0.67). There were no glycemic outcome differences between those using the two different study pumps (n=69, which was the SAPT-AID user group and 18 AID-AID participants who switched for continuation; and n=25, from the AID-AID group who elected to continue on the pump they used in the primary outcomes phase).

In the initial primary results (first 24 weeks of trial comparing the AID group to the SAPT group), there was a 14 percentage point difference between the groups. In the continuation phase, all used AID and the adjusted mean difference in TIR between AID and the initial SAPT results was a similar 12.10 percentage points (95% CI, p<0.001, SD 8.40).

Similar to the primary phase, there was no DKA or severe hypoglycemia. Long-term use (over 48 weeks, representing 69 person-years) did not detect any rare severe adverse events.

CREATE results from the full 48 weeks on open source AID with both SAPT (control) and AID (intervention) groups plotted on the graph.

Conclusion of the continuation study from the CREATE trial

In conclusion, the continuation study from the CREATE trial found that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS is efficacious and safe with various hardware (pumps), and demonstrates sustained glycaemic improvements without additional safety concerns.

Key points to takeaway:

  • Over 48 weeks total of the study (6 months or 24 weeks in the primary phase; 6 months/24 weeks in the continuation phase), there were 64 person-years of use of open source AID in the study, compared to 59 person-years of use of sensor-augmented pump therapy.
  • A variety of pump hardware options were used in the primary phase of the study among the SAPT group, due to hardware (pump) availability limitations. Different pumps were also used in the SAPT-AID group during the AID continuation phase, compared to the pumps available in the AID-AID group throughout both phases of trial. (Also, 18/42 of AID-AID participants chose to switch to the other pump type during the continuation phase).
  • The similar TIR results (14 percentage points difference in primary and 12 percentage points difference in continuation phase between AID and SAPT groups) shows durability of the open source AID and algorithm used, regardless of pump hardware.
  • The SAPT-AID group achieved similar TIR results at the end of their first 6 months of use of AID when compared to the AID-AID group at both their initial 6 months use and their total 12 months/48 weeks of use at the end of the continuation phase.
  • The safety data showed no DKA or severe hypoglycemia in either the primary phase or the continuation phases.
  • Glycemic improvements from this version of open source AID (the OpenAPS algorithm in a modified version of AndroidAPS) are not only immediate but also sustained, and do not increase safety concerns.
CREATE Trial Continuation Results were presented at #EASD2022 on 48 weeks of use of open source AID

New Research on Glycemic Variability Assessment In Exocrine Pancreatic Insufficiency (EPI) and Type 1 Diabetes

I am very excited to share that a new article I wrote was just published, looking at glycemic variability in data from before and after pancreatic enzyme replacement therapy (PERT) was started in someone with type 1 diabetes with newly discovered exocrine pancreatic insufficiency (EPI or PEI).

If you’re not aware of exocrine pancreatic insufficiency, it occurs when the pancreas no longer produces the amount of enzymes necessary to fully digest food. If that occurs, people need supplementary enzymes, known as pancreatic enzyme replacement therapy (PERT), to help them digest their food. (You can read more about EPI here, and I have also written other posts about EPI that you can find at DIYPS.org/EPI.)

But, like MANY medications, when someone with type 1 diabetes or other types of insulin-requiring diabetes starts taking them, there is little to no guidance about whether these medications will change their insulin sensitivity or otherwise impact their blood glucose levels. No guidance, because there are no studies! In part, this may be because of the limited tools available at the time these medications were tested and approved for their current usage. Also this is likely in part because people with diabetes make up a small fraction of the study participants that most of these medications are tested on. If there are any specific studies on the medications in people with diabetes, these studies likely were done before CGM, so little data is available that is actionable.

As a result, the opportunity came up to review someone’s data who happened to have blood glucose data from a continuous glucose monitor (CGM) as well as a log of what was eaten (carbohydrate entries) prior to commencing pancreatic enzyme replacement therapy. The tracking continued after commencing PERT and was expanded to also include fat and protein entries. As a result, there was a useful dataset to compare the impacts of pancreatic enzyme replacement therapy on blood glucose outcomes and specifically, looking at glycemic variability changes!

(You can read an author copy here of the full paper and also see the supplementary material here, and the DOI for the paper is https://doi.org/10.1177/19322968221108414 . Otherwise, below is my summary of what we did and the results!)

In addition to the above background, it’s worth noting that Type 1 diabetes is known to be associated with EPI. In upwards of 40% of people with Type 1 diabetes, elastase levels are lowered, which in other cases is correlated with EPI. However, in T1D, there is some confusion as to whether this is always the case or not. Based on recent discussions with endocrinologists who treat patients with T1D and EPI (and have patients with lowered elastase that they think don’t have EPI), I don’t think there have been enough studies looking at the right things to assess whether people with T1D and lowered elastase levels would benefit from PERT and thus have EPI. More on this in the future!

Because we now have technology such as AID (automated insulin delivery) and CGM, it’s possible to evaluate things beyond simple metrics of “average blood sugar” or “A1c” in response to taking new medications. In this paper, we looked at some basic metrics like average blood sugar and percent time in range (TIR), but we also did quite a few calculations of variables that tell us more about the level of variability in glucose levels, especially in the time frames after meals.

Methods

This person had tracked carb entries through an open source AID system, and so carb entries and BG data were available from before they started PERT. We call this “pre-PERT”, and selected 4 weeks worth of data to exclude major holidays (as diet is known to vary quite a bit during those times). We then compared this to “post-PERT”, the first 4 weeks after the person started PERT. The post-PERT data not only included BGs and carb entries, but also had fat and protein entries as well as PERT data. Each time frame included 13,975 BG data points.

We used a series of open source tools to get the data (Nightscout -> Nightscout Data Transfer Tool -> Open Humans) and process the data (my favorite Unzip-Zip-CSVify-OpenHumans-data.sh script).

All of our code for this paper is open source, too! Check it out here. We analyzed time in range, TIR 70-180, time out of range, TOR >180, time below range, TBR <70 and <54, the number of hyperglycemic excursions >180. We also calculated total daily dose of insulin, average carbohydrate intake, and average carbohydrate entries per day. Then we calculated a series of variability related metrics such as Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), Coefficient of Variation (CV), Standard Deviation (SD), and J_index (which stresses both the importance of the mean level and variability of glycemic levels).

Results

This person already had an above-goal TIR. Standard of care goal for TIR is >70%; before PERT they had 92.12% TIR and after PERT it was 93.70%. Remember, this person is using an open source AID! TBR <54 did not change significantly, TBR <70 decreased slightly, and TOR >180 also decreased slightly.

More noticeably, the total number of unique excursions above 180 dropped from 40 (in the 4 weeks without PERT) to 26 (in 4 weeks when using PERT).

The paper itself has a few more details about average fat, protein, and carb intake and any changes. Total daily insulin was relatively similar, carb intake decreased slightly post-PERT but was trending back upward by the end of the 4 weeks. This is likely an artifact of being careful to adjust to PERT and dose effectively for PERT. The number of meals decreased but the average carb entry per meal increased, too.

What I find really interesting is the assessment we did on variability, overall and looking at specific meal times. The breakfast meal was identical during both time periods, and this is where you can really SEE visible changes pre- and post-PERT. Figure 2 (displayed below), shows the difference in the rate of change frequency. There’s less of the higher rate of changes (red) post-PERT than there is from pre-PERT (blue).

Figure 2 from GV analysis on EPI, showing lower frequency of high rate of change post-PERT

Similarly, figure 3 from the paper shows all glucose data pre- and post-PERT, and you can see the fewer excursions >180 (blue dotted line) in the post-PERT glucose data.

Figure 3 from GV analysis paper on EPI showing lower number of excursions above 180 mg/dL

Table 1 in the paper has all the raw data, and Figure 1 plots the most relevant graphs side by side so you can see pre- and post-PERT before and after after all meals on the left, versus pre and post-PERT before and after breakfast only. Look at TOR >180 and the reduction in post-breakfast levels after PERT! Similarly, HBGI post-PERT after-breakfast is noticeably different than HBGI pre-PERT after-breakfast.

Here’s a look at the HBGI for breakfast only, I’ve highlighted in purple the comparison after breakfast for pre- and post-PERT:

High Blood Glucose Index (HBGI) pre- and post-PERT for breakfast only, showing reduction in post-PERT after breakfast

Discussion

This is a paper looking at n=1 data, but it’s not really about the n=1 here. (See the awesome limitation section for more detail, where I point out it’s n=1, it’s not a clinical study, the person has ‘moderate’ EPI, there wasn’t fat/protein data from pre-PERT, it may not be representative of all people with diabetes with EPI or EPI in general.)

What this paper is about is illustrating the types of analyses that are possible, if only we would capture and analyze the data. There are gaping holes in the scientific knowledge base: unanswered and even unasked questions about what happens to blood glucose with various medications, and this data can help answer them! This data shows minimal changes to TIR but visible and significant changes to post-meal glycemic variability (especially after breakfast!). Someone who had a lower TIR or wasn’t using an open source AID may have more obvious changes in TIR following PERT commencement.

This paper shows several ways we can more easily detect efficacy of new-onset medications, whether it is enzymes for PERT or other commonly used medications for people with diabetes.

For example, we could do a similar study with metformin, looking at early changes in glycemic variability in people newly prescribed metformin. Wouldn’t it be great, as a person with diabetes, to be able to more quickly resolve the uncertainty of “is this even working?!” and not have to suffer through potential side effects for 3-6 months or longer waiting for an A1c lab test to verify whether the metformin is having the intended effects?

Specifically with regards to EPI, it can be hard for some people to tell if PERT “is working”, because they’re asymptomatic, they are relying on lab data for changes in fat soluble vitamin levels (which may take time to change following PERT commencement), etc. It can also be hard to get the dosing “right”, and there is little guidance around titrating in general, and no studies have looked at titration based on macronutrient intake, which is something else that I’m working on. So, having a method such as these types of GV analysis even for a person without diabetes who has newly discovered EPI might be beneficial: GV changes could be an earlier indicator of PERT efficacy and serve as encouragement for individuals with EPI to continue PERT titration and arrive at optimal dosing.

Conclusion

As I wrote in the paper:

It is possible to use glycemic variability to assess changes in glycemic outcomes in response to new-onset medications, such as pancreatic enzyme replacement therapy (PERT) in people with exocrine pancreatic insufficiency (EPI) and insulin-requiring diabetes. More studies should use AID and CGM data to assess changes in glycemic outcomes and variability to add to the knowledge base of how medications affect glucose levels for people with diabetes. Specifically, this n=1 data analysis demonstrates that glycemic variability can be useful for assessing post-PERT response in someone with suspected or newly diagnosed EPI and provide additional data points regarding the efficacy of PERT titration over time.

I’m super excited to continue this work and use all available datasets to help answer more questions about PERT titration and efficacy, changes to glycemic variability, and anything else we can learn. For this study, I collaborated with the phenomenal Arsalan Shahid, who serves as technology solutions lead at CeADAR (Ireland’s Centre for Applied AI at University College Dublin), who helped make this study and paper possible. We’re looking for additional collaborators, though, so feel free to reach out if you are interested in working on similar efforts or any other research studies related to EPI!

A DIY Fuel Enzyme Macronutrient Tracker for Running Ultras (Ultramarathons)

It takes a lot of energy to run ultramarathons (ultras).

To ensure they have enough fuel to complete the run, people usually want to eat X-Y calories per hour, or A-B carbs per hour, while running ultramarathons. It can be hard to know if you’re staying on top of fueling, especially as the hours drag on and your brain gets tired; plus, you can be throwing away your trash as you go so you may not have a pile of wrappers to tell you what you ate.

During training, it may be useful to have a written record of what you did for each run, so you can establish a baseline and work on improving your fueling if that’s something you want to focus on.

For me specifically, I also find it helpful to record what enzyme dosing I am taking, as I have EPI (exocrine pancreatic insufficiency, which you can read more about here) and if I have symptoms it can help me identify where my dosing might have been off from the previous day. It’s not only the amount of enzymes but also the timing that matters, alongside the timing of carbs and insulin, because I have type 1 diabetes, celiac, and EPI to juggle during runs.

Previously, I’ve relied on carb entries to Nightscout (an open source CGM remote monitoring platform which I use for visualizing diabetes data including OpenAPS data) as a record of what I ate, because I know 15g of carbs tracks to a single serving of chili cheese Fritos that are 10g of fat and 2g of protein, and I take one lipase-only and one pancrelipase (multi-enzyme) pill for that; and 21g of carbs is a Honey Stinger Gluten Free Stroopwaffle that is 6g of fat and 1g of protein, and I typically take one lipase-only. You can see from my most recent ultra (a 50k) where I manually took those carb entries and mapped them on to my blood sugar (CGM) graph to visualize what happened in terms of fuel and blood sugar over the course of my ultra.

However, that was “just” a 50k and I’m working toward bigger runs: a 50 mile, maybe a 100k (62 miles), and/or a 100 mile, which means instead of running for 7-8 hours I’ll be running for 12-14 and 24-30(ish) hours! That’s a lot of fuel to need to eat, and to keep track of, and I know from experience my brain starts to get tired of thinking about and eating food around 7 hours. So, I’ll need something better to help me keep track of fuel, enzymes, and electrolytes over the course of longer runs.

I also am planning on being well supported by my “crew” – my husband Scott, who will e-bike around the course of my ultra or my DIY ultra loops and refill my pack with water and fuel. In some cases, with a DIY ultra, he’ll be bringing food from home that I pre-made and he warms up in the microwave.

One of the strategies I want to test is for him to actually hand me the enzymes for the food he’s bringing me. For example, hand me a baggie of mashed potatoes and also hand me the one multi-enzyme (pancrelipase, OTC) pill I need to go with it. That reduces mental effort for me to look up or remember what enzyme amount I take for mashed potatoes; saves me from digging out my baggie of enzymes and having to get the pill out and swallow it, put the baggie away without dropping it, all while juggling the snack in my hands.

He doesn’t necessarily know the counts of enzymes for each fuel (although he could reproduce it, it’s better if I pre-make a spreadsheet library of my fuel options and that helps me both just pick it off a drop down and have an easy reference for him to glance at. He won’t be running 50-100 miles, but he will be waking up every 2-3 hours overnight and that does a number on his brain, too, so it’s easier all around if he can just reference the math I’ve already done!

So, for my purposes: 1) easy tracking of fuel counts for real-time “am I eating according to plan” and 2) retrospective “how did I do overall and should I do something next time” and 3) for EPI and BG analysis (“what should I do differently if I didn’t get the ideal outcome?”), it’s ideal to have a tracking spreadsheet to log my fuel intake.

Here’s what I did to build my ultimate fuel self-tracking self-populating spreadsheet:

First, I created a tab in my spreadsheet as a “Fuel Library”, where I listed out all of my fuel. This ranges from snacks (chili cheese Fritos; Honey Stinger Gluten Free Stroopwaffle; yogurt-covered pretzels, etc.); to fast-acting carbs (e.g. Airhead Minis, Circus Peanuts) that I take for fixing blood sugars; to other snack/treats like chocolate candy bars or cookies; as well as small meals and warm food, such as tomato soup or part of a ham and cheese quesadilla. (All gluten free, since I have celiac. Everything I ever write about is always gluten free!)

After I input the list of snacks, I made columns to input the sodium, calories, fat, protein, and carb counts. I don’t usually care about calories but a lot of recommendations for ultras are calories/hr and carbs/hr. I tend to be lower on the carb side in my regular daily consumption and higher on fat than most people without T1D, so I’m using the calories for ultrarunning comparison to see overall where I’m landing nutrient-wise without fixating on carbs, since I have T1D and what I personally prefer for BG management is likely different than those without T1D.

I also input the goal amount of enzymes. I have three different types of pills: a prescription pancrelipase (I call PERT, which stands for pancreatic enzyme replacement therapy, and when I say PERT I’m referring to the expensive, prescription pancrelipase that’s been tested and studied for safety and efficacy in EPI); an over-the-counter (OTC) lipase-only pill; and an OTC multi-enzyme pancrelipase pill that contains much smaller amounts of all three enzymes (lipase, protease, amylase) than my PERT but hasn’t been tested for safety and efficacy for EPI. So, I have three enzyme columns: Lipase, OTC Pancrelipase, and PERT. For each fuel I calculate which I need (usually one lipase, or a lipase plus a OTC pancrelipase, because these single servings are usually fairly low fat and protein; but for bigger meal-type foods with more protein I may ‘round up’ and choose to take a full PERT, especially if I eat more of it), and input the number in the appropriate column.

Then, I opened another tab on my spreadsheet. I created a row of headers for what I ate (the fuel); time; and then all the macronutrients again. I moved this down to row 3, because I also want to include at the top of the spreadsheet a total of everything for the day.

Example-DIY-Fuel-Enzyme-Tracker-ByDanaMLewis

In Column A, I selected the first cell (A4) for me, then went to Data > Data Validation and clicked on it. It opens this screen, which I input the following – A4 is the cell I’m in for “cell range”, the criteria is “list from a range”, and then I popped over to the tab with my ‘fuel library’ and highlighted the relevant data that I wanted to be in the menu: Food. So that was C2-C52 for my list of food. Make sure “show dropdown list in cell” is marked, because that’s what creates the dropdown in the cell. Click save.

Use the data validation section to choose to show a dropbox in each cell

You’ll want to drag that down to apply the drop-down to all the cells you want. Mine now looked like this, and you can see clicking the dropdown shows the menu to tap on.

Clicking a dropbox in the cell brings up the "menu" of food options from my Fuel Library tab

After I selected from my menu, I wanted column B to automatically put in the time. This gets obnoxious: google sheets has NOW() to put in the current time, but DO NOT USE THIS as the formula updates with the latest time any time you touch the spreadsheet.

I ended up having to use a google script (go to “Extensions” > Apps Script, here’s a tutorial with more detail) to create a function called onEdit() that I could reference in my spreadsheet. I use the old style legacy script editor in my screenshot below.

Older style app script editor for adding scripts to spreadsheet, showing the onEdit() function (see text below in post for what the script is)

Code I used, if you need to copy/paste:

function onEdit(e) {

var rr = e.range;

var ss = e.range.getSheet();

var headerRows = 2;  // # header rows to ignore

if (rr.getRow() <= headerRows) return;

var row = e.range.getRow();

var col = e.range.getColumn();

if(col == 1){

e.source.getActiveSheet().getRange(row,2).setValue(new Date());

}

}

After saving that script (File > Save), I went back to my spreadsheet and put this formula into the B column cells: =IFERROR(onEdit(),””). It fills in the current date/time (because onEdit tells it to if the A cell has been updated), and otherwise sits with a blank until it’s been changed.

Note: if you test your sheet, you’ll have to go back and paste in the formula to overwrite the date/time that gets updated by the script. I keep the formula without the “=” in a cell in the top right of my spreadsheet so I can copy/paste it when I’m testing and updating my sheet. You can also find it in a cell below and copy/paste from there as well.

Next, I wanted to populate my macronutrients on the tracker spreadsheet. For each cell in row 4, I used a VLOOKUP with the fuel name from A4 to look at the sheet with my library, and then use the column number from the fuel library sheet to reference which data element I want. I actually have things in a different order in my fuel library and my tracking sheet; so if you use my template later on or are recreating your own, pay attention to matching the headers from your tracker sheet and what’s in your library. The formula for this cell ended up being “=IFERROR(VLOOKUP(A4,’Fuel Library’!C:K,4, FALSE),””)”, again designed to leave the column blank if column A didn’t have a value, but if it does have a value, to prefill the number from Column 4 matching the fuel entry into this cell. Columns C-J on my tracker spreadsheet all use that formula, with the updated values to pull from the correctly matching column, to pre-populate my counts in the tracker spreadsheet.

Finally, the last thing I wanted was to track easily when I last ate. I could look at column B, but with a tired brain I want something more obvious that tracks how long it’s been. This also is again to maybe help Scott, who will be tasked with helping me stay on top of things, be able to check if I’m eating regularly and encourage me gently or less gently to be eating more as the hours wear on in my ultras.

I ended up creating a cell in the header that would track the last entry from column B. To do this, the formula I found is “INDEX(B4:B,MATCH(143^143,B4:B))”, which checks for the last number in column B starting in B4 and onward. It correctly pulls in the latest timestamp on the list.

Then, in another cell, I created “=NOW()-B2”, which is a good use for the NOW() formula I warned about, because it’s constantly updating every time the sheet gets touched, so that any time I go to update it’ll tell me how long it’s been since between now and the last time I ate.

But, that only updates every time I update the sheet, so if I want to glance at the sheet, it will be only from the last time I updated it… which is not what I want. To fix it, I need to change the autorefresh calculation settings. Go to File > Settings. Click “Calculations” tab, and the first drop down you want to change to be “On change and every minute”.

Under File > Settings there is a "Calculate" tab with a dropdown menu to choose to update on change plus every minute

Now it does what I want, updating that cell that uses the NOW() formula every minute, so this calculation is up to date even when the sheet hasn’t been changed!

However, I also decided I want to log electrolytes in my same spreadsheet, but not include it in my top “when did I last eat” calculator. So, I created column K and inserted the formula IF(A4=”Electrolytes”,””,B4), which checks to see if the Dropdown menu selection was Electrolytes. If so, it doesn’t do anything. If it’s not electrolytes, it repeats the B4 value, which is my formula to put the date and time. Then, I changed B2 to index and match on column K instead of B. My B2 formula now is INDEX(K4:K,MATCH(143^143,K4:K)), because K now has the food-only list of date and time stamps that I want to be tracking in my “when did I last eat” tracker. (If you don’t log electrolytes or don’t have anything else to exclude, you can keep B2 as indexing and matching on column B. But if you want to exclude anything, you can follow my example of using an additional column (my K) to check for things you do want to include and exclude the ones you don’t want. Also, you can hide columns if you don’t want to see them, so column K (or your ‘check for exclusions’ column wherever it ends up) could be hidden from view so it doesn’t distract your brain.

I also added conditional formatting to my tracker. Anytime A2, the time since eaten cell, is between 0-30 minutes, it’s green: indicating I’m on top of my fueling. 30-45 minutes it turns yellow as a warning that it’s time to eat. After 45 minutes, it’ll turn light red as a strong reminder that I’m off schedule.

I kept adding features, such as totaling my sodium consumption per hour, too, so I could track electrolytes+fuel sodium totals. Column L gets the formula =IF(((ABS((NOW()-B4))*1440)<60),F4,””) to check for the difference between the current time and the fuel entry, multiplying it by 1440 to convert to minutes and checking to see that it’s less than 60 minutes. If it is, then it prints the sodium value, and otherwise leaves it blank. (You could skip the ABS part as I was testing current, past, and future values and wanted it to stop throwing errors for future times that were calculated as negatives in the first argument). I then in C2 calculate the sum of those values for the total sodium for that hour, using =SUM(L4:L)

(I thought about tracking the past sodium per hour values to average and see how I did throughout the run on an hourly basis…but so far on my 3 long runs where I’ve used the spreadsheet, the very fact that I am using the tracker and glancing at the hourly total has kept me well on top of sodium and so I haven’t need that yet. However, if I eventually start to have long enough runs where this is an issue, I’ll probably go back and have it calculate the absolute hour sodium totals for retrospective analysis.)

This works great in the Google Sheets app on my phone, which is how I’ll be updating it during my ultras, although Scott can have it open on a browser tab when he’s at home working at his laptop. Every time I go for a long training run, I duplicate the template tab and label it with the date of the run and use it for logging my fueling.

(PS – if you didn’t know, you can rearrange the order of tabs in your sheet, so you can drag the one you want to be actively using to the left. This is useful in case the app closes on your phone and you’re re-opening the sheet fresh, so you don’t have to scroll to re-find the correct tab you want to be using for that run. In a browser, you can either drag and drop the tabs, or click the arrow next to the tab name and select “move left” or “move right”.)

Clicking the arrow to the right of a tab name in google sheets brings up a menu that includes the option to move the tab left or right

Click here to make a copy of my spreadsheet.

If you click to make a copy of a google spreadsheet, it pops up a link confirming you want to make a copy, and also might bring the app script functionality with it. If so, you can click the button to view the script (earlier in the blog post). If it doesn't include the warning about the script, remember to add the script yourself after you make a copy.

Take a look at my spreadsheet after you make a copy (click here to generate a copy if you didn’t use the previous mentioned link), and you’ll note in the README tab a few reminders to modify the fuel library and make sure you follow the steps to ensure that the script is associated with the sheet and validation is updated.

Obviously, you may not need lipase/pancrelipase/PERT and enzyme counts; if you do, your counts of enzymes needed and types of enzyme and quantity of enzymes will need updating; you may not need or want all of these macronutrients; and you’ll definitely be eating different fuel than I am, so you can update it however you like with what you’re eating and what you want to track.

This spreadsheet and the methods for building it can also be used for other purposes, such as tracking wait times or how long it took you to do something, etc.

(If you do find this blog post and use this spreadsheet concept, let me know – I’d love to hear if this is useful for you!)

Findings from the world’s first RCT on open source AID (the CREATE trial) presented at #ADA2022

September 7, 2022 UPDATEI’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or view an author copy here. You can also see a Twitter thread here, if you are interested in sharing the study with your networks.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NEJMoa2203913


(You can also see a previous Twitter thread here summarizing the study results, if you are interested in sharing the study with your networks.)

TLDR: The CREATE Trial was a multi-site, open-labeled, randomized, parallel-group, 24-week superiority trial evaluating the efficacy and safety of an open-source AID system using the OpenAPS algorithm in a modified version of AndroidAPS. Our study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14 percentage points higher among those who used the open-source AID system (95% confidence interval [CI], 9.2 to 18.8; P<0.001) compared to those who used sensor augmented pump therapy; a difference that corresponds to 3 hours 21 minutes more time spent in target range per day. The system did not contribute to any additional hypoglycemia. Glycemic improvements were evident within the first week and were maintained over the 24-week trial. This illustrates that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID. This study concluded that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS, a widely used open-source AID solution, is efficacious and safe.

The backstory on this study

We developed the first open source AID in late 2014 and shared it with the world as OpenAPS in February 2015. It went from n=1 to (n=1)*2 and up from there. Over time, there were requests for data to help answer the question “how do you know it works (for anybody else)?”. This led to the first survey in the OpenAPS community (published here), followed by additional retrospective studies such as this one analyzing data donated by the community,  prospective studies, and even an in silico study of the algorithm. Thousands of users chose open source AID, first because there was no commercial AID, and later because open source AID such as the OpenAPS algorithm was more advanced or had interoperability features or other benefits such as quality of life improvements that they could not find in commercial AID (or because they were still restricted from being able to access or afford commercial AID options). The pile of evidence kept growing, and each study has shown safety and efficacy matching or surpassing commercial AID systems (such as in this study), yet still, there was always the “but there’s no RCT showing safety!” response.

After Martin de Bock saw me present about OpenAPS and open source AID at ADA Scientific Sessions in 2018, we literally spent an evening at the dinner table drawing the OpenAPS algorithm on a napkin at the table to illustrate how OpenAPS works in fine grained detail (as much as one can do on napkin drawings!) and dreamed up the idea of an RCT in New Zealand to study the open source AID system so many were using. We sought and were granted funding by New Zealand’s Health Research Council, published our protocol, and commenced the study.

This is my high level summary of the study and some significant aspects of it.

Study Design:

This study was a 24-week, multi-centre randomized controlled trial in children (7–15 years) and adults (16–70 years) with type 1 diabetes comparing open-source AID (using the OpenAPS algorithm within a version of AndroidAPS implemented in a smartphone with the DANA-i™ insulin pump and Dexcom G6® CGM), to sensor augmented pump therapy. The primary outcome was change in the percent of time in target sensor glucose range (3.9-10mmol/L [70-180mg/dL]) from run-in to the last two weeks of the randomized controlled trial.

  • This is a LONG study, designed to look for rare adverse events.
  • This study used the OpenAPS algorithm within a modified version of AndroidAPS, meaning the learning objectives were adapted for the purpose of the study. Participants spent at least 72 hours in “predictive low glucose suspend mode” (known as PLGM), which corrects for hypoglycemia but not hyperglycemia, before proceeding to the next stage of closed loop which also then corrected for hyperglycemia.
  • The full feature set of OpenAPS and AndroidAPS, including “supermicroboluses” (SMB) were able to be used by participants throughout the study.

Results:

Ninety-seven participants (48 children and 49 adults) were randomized.

Among adults, mean time in range (±SD) at study end was 74.5±11.9% using AID (Δ+ 9.6±11.8% from run-in; P<0.001) with 68% achieving a time in range of >70%.

Among children, mean time in range at study end was 67.5±11.5% (Δ+ 9.9±14.9% from run-in; P<0.001) with 50% achieving a time in range of >70%.

Mean time in range at study end for the control arm was 56.5±14.2% and 52.5±17.5% for adults and children respectively, with no improvement from run-in. No severe hypoglycemic or DKA events occurred in either arm. Two participants (one adult and one child) withdrew from AID due to frustrations with hardware issues.

  • The pump used in the study initially had an issue with the battery, and there were lots of pumps that needed refurbishment at the start of the study.
  • Aside from these pump issues, and standard pump site/cannula issues throughout the study (that are not unique to AID), there were no adverse events reported related to the algorithm or automated insulin delivery.
  • Only two participants withdrew from AID, due to frustration with pump hardware.
  • No severe hypoglycemia or DKA events occurred in either study arm!
  • In fact, use of open source AID improved time in range without causing additional hypoglycemia, which has long been a concern of critics of open source (and all types of) AID.
  • Time spent in ‘level 1’ and ‘level 2’ hyperglycemia was significantly lower in the AID group as well compared to the control group.

In the primary analysis, the mean (±SD) percentage of time that the glucose level was in the target range (3.9 – 10mmol/L [70-180mg/dL]) increased from 61.2±12.3% during run-in to 71.2±12.1% during the final 2-weeks of the trial in the AID group and decreased from 57.7±14.3% to 54±16% in the control group, with a mean adjusted difference (AID minus control at end of study) of 14.0 percentage points (95% confidence interval [CI], 9.2 to 18.8; P<0.001). No age interaction was detected, which suggests that adults and children benefited from AID similarly.

  • The CREATE study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy.
  • This difference reflects 3 hours 21 minutes more time spent in target range per day!
  • For children AID users, they spent 3 hours 1 minute more time in target range daily (95% CI, 1h 22m to 4h 41m).
  • For adult AID users, they spent 3 hours 41 minutes more time in target range daily (95% CI, 2h 4m to 5h 18m).
  • Glycemic improvements were evident within the first week and were maintained over the 24-week trial. Meaning: things got better quickly and stayed so through the entire 24-week time period of the trial!
  • AID was most effective at night.
Difference between control and AID arms overall, and during day and night separately, of TIR for overall, adults, and kids

One thing I think is worth making note of is that one criticism of previous studies with open source AID is regarding the self-selection effect. There is the theory that people do better with open source AID because of self-selection and self-motivation. However, the CREATE study recruited a diverse cohort of participants, and the study findings (as described above) match all previous reports of safety and efficacy outcomes from previous studies. The CREATE study also found that the greatest improvements in TIR were seen in participants with lowest TIR at baseline. This means one major finding of the CREATE study is that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID.

This therefore means there should be NO gatekeeping by healthcare providers or the healthcare system to restrict AID technology from people with insulin-requiring diabetes, regardless of their outcomes or experiences with previous diabetes treatment modalities.

There is also no age effect observed in the trail, meaning that the results of the CREATE Trial demonstrated that open-source AID is safe and effective in children and adults with type 1 diabetes. If someone wants to use open source AID, they would likely benefit, regardless of age or past diabetes experiences. If they don’t want to use open source AID or commercial AID…they don’t have to! But the choice should 100% be theirs.

In summary:

  • The CREATE trial was the first RCT to look at open source AID, after years of interest in such a study to complement the dozens of other studies evaluating open source AID.
  • The conclusion of the CREATE trial is that open-source AID using the OpenAPS algorithm within a version of AndroidAPS, a widely used open-source AID solution, appears safe and effective.
  • The CREATE trial found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy; a difference that reflects 3 hours 21 minutes more time spent in target range per day.
  • The study recruited a diverse cohort, yet still produced glycemic outcomes consistent with existing open-source AID literature, and that compare favorably to commercially available AID systems. Therefore, the CREATE Trial indicates that a range of people with type 1 diabetes might benefit from open-source AID solutions.

Huge thanks to each and every participant and their families for their contributions to this study! And ditto, big thanks to the amazing, multidisciplinary CREATE study team for their work on this study.


September 7, 2022 UPDATE – I’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or like all of the research I contribute to, access an author copy on my research paper.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NE/Moa2203913

Note that the continuation phase study results are slated to be presented this fall at another conference!

Findings from the RCT on open source AID, the CREATE Trial, presented at #ADA2022

AID (APS) book now available in French!

Thanks to the dedicated efforts of Olivier Legendre and Dr. Mihaela Muresan, my book “Automated Insulin Delivery: How artificial pancreas “closed loop” systems can aid you in living with diabetes” (available on Amazon in Kindle, paperback, and hardcover formats, or free to read online and download at ArtificialPancreasBook.com) is now available in French!

The French version is also available for free download as a PDF at ArtificialPancreasBook.com or in Kindle (FR), paperback (FR), and hardcover (FR) formats!

 

French version of the AID book is now available, also in hardcover, paperback, and Kindle formats on Amazon

Merci au Dr. Mihaela Muresan et Olivier Legendre pour la traduction de l’intégralité de ce livre !

(Thank you to Dr. Mihaela Muresan and Olivier Legendre for translating this entire book!)

Why it feels harder to dose pancreatic enzyme replacement therapy (PERT) than insulin

In 2002 when I was diagnosed with Type 1 diabetes, I struggled with being handed a vial of insulin and told vaguely to eat X amount of food and take Y amount of insulin. There was no ability to eat more and adjust the dose accordingly. It was frustrating. The only tool I had was a huge (imagine three iPhone 13 or equivalently large smartphones sitting on top of each other) blood glucose meter that took a lot of blood and a long time (a minute or more) to return a single blood glucose data point. The feedback loop wasn’t very useful, even when I tested my blood sugar manually 10-14 times per day.

Thankfully, in the last two decades, diabetes tools have evolved. Meters got smaller, faster, and take less blood. There has also been the devlopment of continuous glucose monitors (CGM) which I can wear and get near real-time readings of glucose data and can see what’s happened in the past. And, paired with an algorithm that also knows about the history of any insulin dosing on my insulin pump, and it can automatically adjust my insulin delivery in real time to predict, prevent, and reduce hypo- and hyperglycemia. (AID is awesome and if you haven’t heard about it, there’s a 4-minute free animated video here that explains it.) Diabetes no longer is quite the headache it was twenty – or even ten – years ago.

But realizing that I have exocrine pancreatic insufficiency (known as EPI or PEI) and learning how to take pancreatic enzyme replacement therapy (known as PERT) is a similar headache to diabetes in 2002.

With insulin, taking too much can cause hypoglycemia (low blood sugar). Taking too little can cause hyperglycemia (high blood sugar). Yet, with diabetes, you can measure blood glucose and see the response to insulin within a minutes-to-hours time frame. You can also use an insulin pump and an automated insulin delivery system to titrate and adjust insulin in real time.

However, for EPI, you need to take enzymes (that your pancreas doesn’t produce enough of) to help you digest your food. Your pancreas makes three types of enzymes: lipase, to help fat digest; protease, to help protein digest; and amylase, to help starches and carbohydrates digest. These are taken by mouth as a pill that you swallow. Together in one pill, it’s called “pancrelipase”, and it’s for pancreatic enzyme replacement therapy (PERT). (I’m personally bad about using pancrelilpase/PERT interchangeably, because PERT is faster to say and type, but it is possible to use standalone enzymes in PERT).

Because they are pills that you have to swallow when you eat, it’s hard to dose. Taking too little means you may have GI-related symptoms in the hours following the meal and feeling bad until the next day or so. Taking too much is expensive, although unlike insulin it’s rare to take “too much” and cause bad side effects (although possible at super high doses). There’s also the “pill burden”, because swallowing a bunch of pills is annoying and sometimes hard, both physically to swallow and to remember to take them throughout your meal.

You also can’t take more hours later if you forgot to take them or realize you didn’t dose enough for that meal. If you underdosed, you underdosed and just get to experience the symptoms that come with it. Sometimes, it’s not clear why you are having symptoms. Because there are three enzymes being replaced, it’s possible that the dosing was off for any one of the three enzymes. But again, there’s no measurement or feedback loop, or a sign that appears saying “you underdosed protease, take more next time”. The best you can do is try different sized meals over time with different doses of PERT, trying to reverse engineer your lipase:fat and protease:protein and amylase:carb ratios and continuously update them as you have new data.

It’s a lot of work, the feedback loop is slow, getting it “wrong” is painful physically and psychologically, and there are no vacations from it. Everything I eat, now that I have EPI, needs enzymes, and given the fact that I have automated insulin delivery to help manage insulin dosing, I am finding PERT to be a lot harder and more annoying (currently).

A comparison of dosing insulin and dosing enzymes. Insulin can cause hypo- or hyperlgycemia but there are tools (CGM and BG meters) and a feedback loop in diabetes. With enzymes, there is no fast feedback loop and underdosing is common. There is no ability to correct an underdose and there are multiple variables that can influence the outcome.

There’s no happy ending to this post, but this is one of the reasons why I am so interested in partnering with researchers to do research on EPI. There are a LOT of improvements that can be made, ranging from improving titration guidance of PERT to testing the efficacy of different over the counter enzymes to finding new technology that might begin to provide a feedback loop into EPI (either for short-term assessment or longer-term use for those who prefer it). If you’re someone interested in this type of research, please don’t hesitate to reach out (Dana@OpenAPS.org).

(PS, if you didn’t see them, I have other posts about EPI at DIYPS.org/EPI)

Looking back at work and accomplishments in 2021

I decided to do a look back at the last year’s worth of work, in part because it was a(nother) weird year in the world and also because, if you’re interested in my work, unless you read every single Tweet, there may have been a few things you missed that are of interest!

In general, I set goals every year that stretch across personal and professional efforts. This includes a daily physical activity streak that coincides with my walking and running lots of miles this year in pursuit of my second marathon and first (50k) ultramarathon. It’s good for my mental and physical health, which is why I post almost daily updates to help keep myself accountable. I also set goals like “do something creative” which could be personal (last year, knitting a new niece a purple baby blanket ticked the box on this goal!) or professional. This year, it was primarily professional creativity that accomplished this goal (more on that below).

Here’s some specifics about goals I accomplished:

RUNNING

  • My initial goal was training ‘consistently and better’ than I did for my first marathon, with 400 miles as my stretch goal if I was successfully training for the marathon. (Otherwise, 200 miles for the year would be the goal without a marathon.) My biggest-ever running year in 2013 with my first marathon was 356 miles, so that was a good big goal for me. I achieved it in June!
  • I completed my second marathon in July, and PR’d by over half an hour.
  • I completed my first-ever ultramarathon, a 50k!
  • I re-set my mileage goal after achieving 400 miles..to 500..600…etc. I ultimately achieved the biggest-ever mileage goal I’ve ever hit and think I ever will hit: I ran 1,000 miles in a single year!
  • I wrote lots of details about my methods of running (primarily, run/walking) and running with diabetes here. If you’re looking for someone to cheer you on as you set a goal for daily activity, like walking, or learning to run, or returning to running…DM or @ me on Twitter (@DanaMLewis). I love to cheer people on as they work toward their activity goals! It helps keep me inspired, too, to keep aiming at my own goals.

CREATIVITY

  • My efforts to be creative were primarily on the professional side this year. The “Convening The Center” project ended up having 2 out of 3 of my things that I categorized as being creative. The first was the design of the digital activities and the experience of CTC overall (more about that here). The second were the items in the physical “kit” we mailed out to participants: we brainstormed and created custom playing cards and physical custom keychains. They were really fun to make, especially in partnership with our excellent project artist, Rebeka Ryvola, who did the actual design work!
  • My third “creative” endeavor was a presentation, but it was unlike the presentations I usually give. I was tasked to create a presentation that was “visually engaging” and would not involve showing my face in the presentation. I’ve linked to the video below in the presentation section, but it was a lot of work to think about how to create a visually and auditory focused presentation and try to make it engaging, and I’m proud of how it turned out!

RESEARCH AND PUBLICATIONS

  • This is where the bulk of my professional work sits right now. I continue to be a PI on the CREATE trial, the world’s first randomized control trial assessing open-source automated insulin delivery technology, including the algorithm Scott and I dreamed up and that I have been using every day for the past 7 years. The first data from the trial itself is forthcoming in 2022. 
  • Convening The Center also was a grant-funded project that we turned into research with a publication that we submitted, assessing more of what patients “do”, which is typically not assessed by researchers and those looking at patient engagement in research or innovation. Hopefully, the publication of the research article we just submitted will become a 2022 milestone! In the meantime, you can read our report from the project here (https://bit.ly/305iQ1W ), as this grant-funded project is now completed.
  • Goal-wise, I aim to generate a few publications every year. I do not work for any organization and I am not an academic. However, I come from a communications background and see the benefit of reaching different audiences where they are, which is why I write blog posts for the patient community and also seek to disseminate knowledge to the research and clinical communities through traditional peer-reviewed literature. You can see past years’ research articulated on my research page (DIYPS.org/research), but here’s a highlight of some of the 2021 publications:
  • Also, although I’m not a traditional academic researcher, I also participate in the peer review process and frequently get asked to peer-review submitted articles to a variety of journals. I skimmed my email and it looks like I completed (at least) 13 peer reviews, most of which included also reviewing subsequent revisions of those submitted articles. So it looks like my rate of peer reviewing (currently) is matching my rate of publishing. I typically get asked to review articles related to open-source or DIY diabetes technology (OpenAPS, AndroidAPS, Loop, Nightscout, and other efforts), citizen science in healthcare, patient-led research or patient engagement in research, digital health, and diabetes data science. If you’re submitting articles on that topic, you’re welcome to recommend me as a potential reviewer.

PRESENTATIONS

  • I continued to give a lot of virtual presentations this year, such as at conferences like the “Insulin100” celebration conference (you can see the copy I recorded of my conference presentation here). I keynoted at the European Patients Forum Congress as well as at ADA’s Precision Diabetes Medicine 2021; an invited talk ADA Scientific Sessions (session coverage here); the 2021 Federal Wearables Summit: (video here); and the BIH Clinician Scientist Symposium (video here), to name a few (but not all).
  • Additionally, as I mentioned, one of the presentations I’m most proud of was created for the Fall 2021 #DData Exchange event:

OTHER STUFF

I did quite a few other small projects that don’t fit neatly into the above categories.

One final thing I’m excited to share is that also in 2021, Amazon came out with a beta program for producing hardcover/hardback books, alongside the ability to print paperback books on demand (and of course Kindle). So, you can now buy a copy of my book about Automated Insulin Delivery: How artificial pancreas “closed loop” systems can aid you in living with diabetes in paperback, hardback, or on Kindle. (You can also, still, read it 100% for free online via your phone or desktop at ArtificialPancreasBook.com, or download a PDF for free to read on your device of choice. Thousands of people have downloaded the PDF!)

Now available in hardcover, the book about Automated Insulin Delivery by Dana M. Lewis